Dask
data-processing library
Library for parallel computing in Python
Supported languages
Pros and Cons
Ventajas
- + Easy parallelization
- + Familiar API (pandas/numpy)
- + Scales to clusters
- + Lazy evaluation
- + API compatible with pandas, NumPy and scikit-learn
- + Scales from laptop to clusters
- + Lazy evaluation for optimization
- + Dashboard for monitoring
- + Integration with PyData ecosystem
- + Supports larger-than-memory data
Desventajas
- - Overhead
- - Complex debugging
- - Overhead for small datasets
- - Complex distributed debugging
- - Not all pandas functions supported
- - Cluster configuration can be difficult
- - Variable performance depending on operation
Casos de Uso
- Big data processing
- Parallel computing
- ETL
- Distributed ML
- Processing larger-than-RAM data
- Parallelizing pandas workflows
- Distributed ETL
- Feature engineering at scale
- Big data exploratory analysis
- Distributed machine learning